Power management, while indispensable for the working of battery-free devices on fragile ambient energy, unfortunately, also entails excessive workloads that consume the scarce harvested energy. Existing efforts aimed at addressing this typically manage to tackle only a fraction of the challenges, leaving power management as a painful Achilles’ heel for battery-free devices. In this paper, we systematically analyze the full-flow of power management and propose μ Man, a painless architecture with no extra workload on battery-free devices. That is, we shift the entire workload of power management from the resource-constrained battery-free devices to the resource-rich gateway. For this goal, we design a near-zero-power sampling-free monitoring mechanism to transparently piggyback the power status of the device directly onto the uplink signal waveform. Based on these real-time statuses, the gateway can take over the required computation and issue the resultant energy allocations back to devices. The design is fully transparent to the devices, and the devices can even remain in deep sleep during the whole process to minimize energy consumption. The experiments show that μ Man can reduce the energy consumption of power management by 97.2%, improve the power efficiency by 53%, and reduce the minimum energy requirements for the device start-up by 5.8 ×.

Recent advances in backscatter communication have exhibited great advantages on uplink, both in power consumption and communication performance. However, their downlink tends to lag far behind due to stringent on-device power constraints. This paper presentsSisyphus, a novel communication paradigm designed to empower backscatter devices with LoRa downlink. To achieve this, we propose a novel receiver design for passive coherent demodulation of LoRa. In this design, we creatively couple LoRa’s down-conversion with de-chirping (dc2), leveraging the processing gain brought by chirp spread spectrum (CSS) modulation to boost communication range without the need for additional power supply. Moreover, we exploit the cyclical time-frequency feature intrinsic to LoRa for demodulation, and a low-power analog-digital signal processing circuit with negligible power is devised to replace the existing power-intensive sampling and costly digital computation. We prototype Sisyphus for proof-of-concept, and comprehensive experimental results demonstrate that Sisyphus can achieve significant power savings compared to legacy LoRa receiver while retaining the anti-interference ability of legacy LoRa. We envision that the design of Sisyphus can unlock the potential for broader applications of LoRa-based backscatter devices.

Battery-free nodes harvest ambient energy, accelerating large-scale IoT (Internet of Things) deployment. However, sporadic beginnings and ends of power failures impede collaboration, obstructing the execution of complex applications. The prior collaborative protocols have high energy demands and lack scalability. This paper introduces Cupid, a novel scheduling architecture that employs a coordinator device to circumvent the collaborative energy bottleneck, enhancing the scalability of battery-free node collaboration. Cupid employs an efficient crosslayer communication protocol to offload energy-intensive tasks to the coordinator. To reduce latency from non-local execution, we propose a predictive scheduling algorithm based on curve fitting. Additionally, we implement a circuit on the node side for ultra-low-power upload and download capabilities. We implement a prototype and conduct extensive evaluations. Compared to the state-of-the-art, it is the first to achieve intermittent coordination in medium-scale EH-WSNs, reducing latency by 94.56%.

The Internet of Things (IoT) can only thrive if IoT sensor nodes can be effortlessly deployed and maintained without compromising their general-purpose nature. However, existing low-power sensor systems fail to strike a balance between these two issues, leaving the widespread of IoT sensor nodes as an open problem. In this paper, we propose LEGO+ as a minimalist yet general-purpose sensing edge-end architecture. Instead of running embedded software on a redundant general-purpose microprocessor, LEGO+ can directly construct the desired control functionality for various IoT sensing applications through hardware-level logic orchestration. To achieve this, we first conduct an in-depth analysis of the underlying unit behaviors within IoT sensor systems and, based on this, abstract a uniform logic orchestration model. Next, to enable sensor nodes to comprehend and execute the generated logic, we devise a hierarchical atomic control circuit with negligible overheads. Finally, we develop a task state prediction scheme to further improve the overall operation efficiency among multiple nodes. We prototype LEGO+ for proof-of-concept and conduct comprehensive experiments, and the results demonstrate that LEGO+ can reduce the overall power consumption of sensor nodes by 86% and enhance task efficiency by 49%, thereby facilitating a wider array of IoT sensing applications.

Large-scale IoT sensor deployment calls for inexpensive, low-power sensor nodes that still perform long-range, large-scale networking at the system level. However, current sensor nodes are constructed according to the 'one-size-fits-all’ embedded design, where the processor and RF transceiver are indispensable but underutilized in low-duty cycles, resulting in overwhelmingly significant unit price and run-time power. In this paper, we propose a novel processor-sharing IoT architecture that converts the vast majority of sensor nodes from embedded computers to low-end RF peripherals. The conventional full-fledged sensor nodes are smashed into the air, and the scattered chips are scaled well with negligible overheads through a virtual I$^{2}$C bus calledRFBus. Specifically, RFBus interface is designed to be backward compatible with the I$^{2}$C bus interface, and thus, RFBus network inherits versatile link layer services transparently from the well-established I$^{2}$C link layer protocol. We design RFBus with joint consideration of system-level performance and deployment costs and evaluate the prototypes both indoors and outdoors. The result indicates that the proposed architecture achieves 6.09 × (indoor) and 6.69 × (outdoor) energy saving and reduces the unit price of sensor nodes by 23.5% (indoor) and 33.5% (outdoor).

Legacy LoRa receiver adopts a superheterodyne architecture with a runtime power consumption of up to 100mW, resulting in its low-power promise can only be delivered in low duty-cycle mode. This paper presents Sisyphus as an ultra-low-power LoRa receiver, ensuring around-the-clock LoRa availability while extending battery life significantly. To achieve this, we propose a novel receiver design for passive coherent demodulation of LoRa. In this design, we creatively couple LoRa's down-conversion with de-chirping (dc2), leveraging the processing gain brought by chirp spread spectrum (CSS) modulation to boost communication range without the need for additional power supply. Moreover, we exploit the cyclical time-frequency feature intrinsic to LoRa for demodulation, and a low-power analog-digital signal processing circuit with negligible power is devised to replace the existing power-intensive sampling and costly digital computation. We prototype Sisyphus for proof-of-concept, and comprehensive experimental results demonstrate that Sisyphus can achieve significant power savings compared to legacy LoRa receiver while retaining the anti-interference ability of legacy LoRa. We envision that the design of Sisyphus can unlock the potential for broader applications of LoRa.

Large-scale IoT sensor deployment calls for inexpensive, low-power sensor nodes that still perform long-range, large-scale networking at the system level. However, current sensor nodes are constructed according to the `one-size-fits-all' embedded design, where the processor and RF transceiver are indispensable but underutilized in low-duty cycles, resulting in overwhelmingly significant unit price and run-time power. In this paper, we propose a novel processor-sharing IoT architecture that converts the vast majority of sensor nodes from embedded computers to low-end RF peripherals. The conventional full-fledged sensor nodes are smashed into the air, and the scattered chips are scaled well with negligible overheads through a virtual I2C bus called RFBus. Specifically, the RFBus interface is designed to be backward compatible with the I2C bus interface, and thus, the RFBus network inherits versatile link layer services transparently from the well-established I2C link layer protocol. We design the RFBus with a joint consideration of system-level performance and deployment costs and evaluate the prototypes in indoor and outdoor scenarios. The result indicates that the proposed architecture achieves 6.09 x (indoor) and 6.69 x (outdoor) energy saving and reduces the unit price of sensor nodes by 23.5% (indoor) and 33.5% (outdoor).

Wi-Fi CSI-based indoor localization systems can realize decimeter-level localization accuracy. However, these systems require that the location and antenna array orientation of Wi-Fi Access Point (AP) are known in advance, which makes it impractical for large-scale deployment. In this paper, we present MapFi, which can realize autonomous mapping of Wi-Fi infrastructure without labor-intensive site survey. To this end, we focus on addressing three problems. First, as there will be diverse layouts of devices and antennas with respective to numerous and heterogeneous Wi-Fi APs, we propose a general method to estimate AoA and generate the Wi-Fi map. Second, while the existing systems can provide a promising median localization accuracy, tail performance is usually far worse. Consequently, we develop a revision method to reduce tail errors. Third, when deployed in large-scale indoor environment, obstacles and long-distance communication might incur failed CSI collection. Therefore, we segment Wi-Fi APs into groups and finally merge these groups to generate the global Wi-Fi map. We conduct experiments in different scenarios to verify the proposed methods. The experimental results show that we can realize the$80\%$localization error within$1.15m$and$0.74m$in office room and open space respectively, which is as accurate as localization systems requiring known Wi-Fi map.